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Towards Building a High-Performance ...

Jameel, Abu Shafin Moham Mahdee,

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Towards Building a High-Performance Intelligent Radio Network Through Deep Learning: Addressing Data Privacy, Adversarial Robustness, Network Structure, and Latency Requirements /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Building a High-Performance Intelligent Radio Network Through Deep Learning: Addressing Data Privacy, Adversarial Robustness, Network Structure, and Latency Requirements // Abu Shafin Moham Mahdee Jameel.
作者:	Jameel, Abu Shafin Moham Mahdee,
面頁冊數:	1 electronic resource (130 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
標題:	Wireless networks. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31345319
ISBN:	9798342107037

Towards Building a High-Performance Intelligent Radio Network Through Deep Learning: Addressing Data Privacy, Adversarial Robustness, Network Structure, and Latency Requirements /
Jameel, Abu Shafin Moham Mahdee,

Towards Building a High-Performance Intelligent Radio Network Through Deep Learning: Addressing Data Privacy, Adversarial Robustness, Network Structure, and Latency Requirements /Abu Shafin Moham Mahdee Jameel. - 1 electronic resource (130 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

With the increasing availability of inexpensive computing power in wireless radio network nodes, machine learning based models are being deployed in operations that traditionally relied on rule-based or statistical methods. Contemporary high bandwidth networks enable easy availability of significant amounts of training data in a comparatively short time, aiding in the development of better deep learning models. Specialized deep learning models developed for wireless networks have been shown to consistently outperform traditional methods in a variety of wireless network applications.We aim to address some of the unique challenges inherent in the wireless radio communication domain. Firstly, as data is transmitted over the air, data privacy and adversarial attacks pose heightened risks. Secondly, due to the volume of data and the time-sensitive nature of the processing that is required, the speed of the machine learning model becomes a significant factor, often necessitating operation within a latency constraint. Thirdly, the impact of diverse and time-varying wireless environments means that any machine learning model also needs to be generalizable. The increasing computing power present in wireless nodes provides an opportunity to offload some of the deep learning to the edge, which also impacts data privacy.Towards this goal, we work on deep learning methods that operate along different aspects of a wireless network-on network packets, error prediction, modulation classification, and channel estimation-and are able to operate within the latency constraint, while simultaneously providing better privacy and security. After proposing solutions that work in a traditional centralized learning environment, we explore edge learning paradigms where the learning happens in distributed nodes.

English

ISBN: 9798342107037Subjects--Topical Terms:

1531264
Wireless networks.

Towards Building a High-Performance Intelligent Radio Network Through Deep Learning: Addressing Data Privacy, Adversarial Robustness, Network Structure, and Latency Requirements /
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